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探索酪氨酸激酶组空间中的序列-结构关系:癌症治疗药物结合特异性机制的功能分类

Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics.

作者信息

Verkhivker Gennady M

机构信息

Department of Pharmaceutical Chemistry, School of Pharmacy, Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047-1620, USA.

出版信息

Bioinformatics. 2007 Aug 1;23(15):1919-26. doi: 10.1093/bioinformatics/btm277. Epub 2007 May 30.

Abstract

MOTIVATION

Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases.

RESULTS

The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding-site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather than being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding-site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.

摘要

动机

超过500种已鉴定的蛋白激酶所共有的进化和结构保守模式,导致了激酶抑制剂交叉反应的复杂序列-结构关系。理解蛋白激酶家族结合特异性的分子基础是癌症治疗药物发现中的核心问题,仍然具有挑战性,因为抑制剂的选择性既不容易从化学蛋白质组学研究中解读出来,也不容易直接从序列或结构信息中辨别出来。我们展示了酪氨酸激酶组空间中序列-结构-结合关系的综合视图,其中激酶结合位点的进化分析与抑制剂-蛋白质相互作用的计算蛋白质组学分析相结合。这种方法为靶向蛋白酪氨酸激酶的癌症药物的结合特异性机制提供了功能分类。

结果

所提出的激酶结合特异性功能分类探索了酪氨酸激酶的结构可塑性和结合位点残基的序列变异与抑制剂的构象偏好相关联以实现有效药物结合的机制。酪氨酸激酶结合特异性的分子基础可能很大程度上由抑制剂对该酶一系列结构不同的构象状态的构象适应性驱动,而不是由它们在激酶组空间中的系统发育接近度或与可变结合位点残基相互作用的差异决定。这种方法通过揭示突出的激酶药物(伊马替尼、达沙替尼和厄洛替尼)的激酶选择性分子基础,在结构生物信息学分析和疾病之间提供了富有成效的功能联系,这与结构和蛋白质组学实验一致。

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